Algorithms for clustering data
Algorithms for clustering data
Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fab: content-based, collaborative recommendation
Communications of the ACM
Siteseer: personalized navigation for the Web
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Recommendation as classification: using social and content-based information in recommendation
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
On the merits of building categorization systems by supervised clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
ACM Computing Surveys (CSUR)
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
A vector space model for automatic indexing
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Information Retrieval
Modern Information Retrieval
Journal of the American Society for Information Science and Technology
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
More than the sum of its members: challenges for group recommender systems
Proceedings of the working conference on Advanced visual interfaces
IEEE Transactions on Knowledge and Data Engineering
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Incremental click-stream tree model: Learning from new users for web page prediction
Distributed and Parallel Databases
Discovery of knowledge flow in science
Communications of the ACM - Two decades of the language-action perspective
Knowledge flow network planning and simulation
Decision Support Systems
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Discovery of textual knowledge flow based on the management of knowledge maps
Concurrency and Computation: Practice & Experience - 2nd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2007)
Optimizing Preferences within Groups: A Case Study on Travel Recommendation
SBIA '08 Proceedings of the 19th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
A Group Recommender System for Tourist Activities
EC-Web 2009 Proceedings of the 10th International Conference on E-Commerce and Web Technologies
Integrating knowledge flow mining and collaborative filtering to support document recommendation
Journal of Systems and Software
A flexible multi criteria information filtering model
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Soft Computing on Web; Guest Editors: A. G. López-Herrera, E. Herrera-Viedma
Content-based recommendation systems
The adaptive web
A theory of information need for information retrieval that connects information to knowledge
Journal of the American Society for Information Science and Technology
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Socially aware tv program recommender for multiple viewers
IEEE Transactions on Consumer Electronics
Hierarchical graph maps for visualization of collaborative recommender systems
Journal of Information Science
QA document recommendations for communities of question-answering websites
Knowledge-Based Systems
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Recommender systems can mitigate the information overload problem and help workers retrieve knowledge based on their preferences. In a knowledge-intensive environment, knowledge workers need to access task-related codified knowledge (documents) to perform tasks. A worker's document referencing behavior can be modeled as a knowledge flow (KF) to represent the evolution of his or her information needs over time. Document recommendation methods can proactively support knowledge workers in the performance of tasks by recommending appropriate documents to meet their information needs. However, most traditional recommendation methods do not consider workers’ KFs or the information needs of the majority of a group of workers with similar KFs. A group's needs may partially reflect the needs of an individual worker that cannot be inferred from his or her past referencing behavior. In other words, the group's knowledge complements that of the individual worker. Thus, we leverage the group perspective to complement the personal perspective by using hybrid approaches, which combine the KF-based group recommendation method (KFGR) with traditional personalized-recommendation methods. The proposed hybrid methods achieve a trade-off between the group-based and personalized methods by exploiting the strengths of both. The results of our experiment show that the proposed methods can enhance the quality of recommendations made by traditional methods. © 2012 Wiley Periodicals, Inc.